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As Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the weather system would intensify into a severe hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had previously made such a bold forecast for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s new DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin explained in his official briefing that Google’s model was a key factor for his certainty: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa reaching a Category 5 hurricane. While I am not ready to predict that strength at this time due to path variability, that remains a possibility.

“It appears likely that a period of rapid intensification is expected as the storm moves slowly over exceptionally hot sea temperatures which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Traditional Models

Google DeepMind is the pioneer AI model focused on hurricanes, and now the initial to beat traditional weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, Google’s model is the best – surpassing experts on track predictions.

Melissa ultimately struck in Jamaica at maximum strength, one of the strongest landfalls recorded in almost 200 years of record-keeping across the Atlantic basin. The confident prediction likely gave people in Jamaica additional preparation time to get ready for the catastrophe, possibly saving people and assets.

How The Model Functions

Google’s model operates through spotting patterns that traditional time-intensive physics-based prediction systems may miss.

“They do it much more quickly than their physics-based cousins, and the computing power is more affordable and demanding,” said Michael Lowry, a ex meteorologist.

“This season’s events has demonstrated in quick time is that the recent artificial intelligence systems are competitive with and, in certain instances, superior than the slower traditional weather models we’ve traditionally leaned on,” Lowry added.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an example of machine learning – a method that has been used in research fields like weather science for a long time – and is distinct from generative AI like ChatGPT.

Machine learning takes large datasets and pulls out patterns from them in a manner that its system only requires minutes to generate an answer, and can operate on a desktop computer – in strong contrast to the primary systems that authorities have used for years that can require many hours to process and require some of the biggest high-performance systems in the world.

Expert Reactions and Upcoming Developments

Still, the fact that Google’s model could exceed previous top-tier legacy models so quickly is truly remarkable to weather scientists who have spent their careers trying to forecast the world’s strongest storms.

“It’s astonishing,” commented James Franklin, a former expert. “The data is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

He noted that while the AI is beating all competing systems on predicting the future path of hurricanes worldwide this year, like many AI models it occasionally gets high-end intensity forecasts inaccurate. It had difficulty with another storm previously, as it was also undergoing quick strengthening to category 5 above the Caribbean.

In the coming offseason, he stated he plans to discuss with the company about how it can make the AI results even more helpful for experts by offering extra under-the-hood data they can utilize to evaluate the reasons it is coming up with its conclusions.

“A key concern that nags at me is that while these predictions seem to be highly accurate, the output of the model is kind of a black box,” said Franklin.

Wider Sector Trends

There has never been a commercial entity that has produced a high-performance forecasting system which grants experts a peek into its methods – in contrast to nearly all other models which are offered free to the public in their full form by the governments that designed and maintain them.

Google is not the only one in adopting AI to solve challenging meteorological problems. The US and European governments are developing their own artificial intelligence systems in the development phase – which have also shown improved skill over previous traditional systems.

Future developments in AI weather forecasts seem to be startup companies taking swings at previously tough-to-solve problems such as long-range forecasts and better early alerts of tornado outbreaks and flash flooding – and they have secured US government funding to pursue this. One company, WindBorne Systems, is even launching its proprietary atmospheric sensors to address deficiencies in the national monitoring system.